Embodiments of the invention relate to the field of data storage, and in particular, to reducing energy consumption and optimizing workload and performance in multi-tier storage systems using extent-level dynamic tiering.
Energy consumption continues to increase in modern data centers because of data growth and new technology development. Energy is an expensive commodity for operations of computing technologies. Energy management is an important consideration for enterprises when architecting an information technology (IT) infrastructure. Data storage is consuming an increasing percentage of data center's energy consumption as the amount of data enterprises store drastically increase. Data storage, in a typical data center, accounts for approximately 40% of the data center's total energy consumption.
Storage tiering is a type of storage architecture that assigns different categories of data to different types of storage media. Storage tiering aims to reduce storage costs to an IT infrastructure, while meeting performance requirements. Tiering categories are primarily based on performance requirements, frequency of use, and levels of protection needed. For example, a first storage tier with expensive high-quality media that provides fault tolerance and reliability (e.g., RAID6, SSD) may be used for mission-critical data. A second storage tier with cheaper and more conventional storage media (e.g., SATA) may be used for non-mission-critical data that is accessed infrequently. A third storage tier with high performance storage (e.g., fiber channel) may be used for frequently accessed data. A fourth storage tier with optical storage (e.g., tape storage) may be used to backup data from another storage tier. Accordingly, storage tiering aligns data's value, importance, and performance requirements with the reliability and performance of the actual storage the data resides on.
Storage workload optimization refers to maximizing utilization of available data storage resources to reduce operating cost and complexity, while meeting data's performance requirements (e.g., serving I/O requests). Performance optimization refers to satisfying data performance requirements and/or achieving the best possible data performance under resource constraints. Workload and performance optimization maximizes storage device and disk utilization by leveraging available storage in an efficient manner, while satisfying data's performance requirements. Storage device and disk utilization directly correlates to storage operational expenses (e.g., energy). For instance, at any given point-in-time all the storage devices in a storage system may not be utilized to near capacity to meet data's required performance. Accordingly, storage workload and performance optimization maximizes a storage system's shared resources, while meeting data's required performance.